Overview

Dataset statistics

Number of variables6
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.0 KiB
Average record size in memory48.1 B

Variable types

NUM6

Reproduction

Analysis started2020-08-25 00:42:22.679908
Analysis finished2020-08-25 00:42:29.252006
Duration6.57 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz5 is highly correlated with oz3High correlation
oz3 is highly correlated with oz5High correlation
oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.463363438844681e-10
Minimum-2.286097288131714
Maximum2.3334527015686035
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:42:29.297703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.286097288
5-th percentile-1.6268875
Q1-0.7855458856
median0.004820458125
Q30.7800930291
95-th percentile1.593421066
Maximum2.333452702
Range4.61954999
Interquartile range (IQR)1.565638915

Descriptive statistics

Standard deviation0.9999999996
Coefficient of variation (CV)2240462856
Kurtosis-0.7857435542
Mean4.463363439e-10
Median Absolute Deviation (MAD)0.7819260061
Skewness-0.04777657942
Sum4.463363439e-07
Variance0.9999999991
2020-08-25T00:42:29.400222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0947399437410.1%
 
0.795076370210.1%
 
0.785838067510.1%
 
0.324556171910.1%
 
-0.911436736610.1%
 
-1.49171400110.1%
 
-0.993097126510.1%
 
0.883472323410.1%
 
1.64194047510.1%
 
1.87240755610.1%
 
-1.9114679110.1%
 
-1.6692743310.1%
 
1.05599105410.1%
 
-0.787177741510.1%
 
-0.346027553110.1%
 
0.317281335610.1%
 
-0.150399595510.1%
 
0.647124648110.1%
 
0.649075686910.1%
 
1.86981940310.1%
 
-1.98310244110.1%
 
0.686177611410.1%
 
-0.934221923410.1%
 
1.64578497410.1%
 
-0.42937496310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.28609728810.1%
 
-2.28038072610.1%
 
-2.26309490210.1%
 
-2.18535590210.1%
 
-2.16015529610.1%
 
-2.14855980910.1%
 
-2.12161421810.1%
 
-2.11744070110.1%
 
-2.11681699810.1%
 
-2.11467957510.1%
 
ValueCountFrequency (%) 
2.33345270210.1%
 
2.24053859710.1%
 
2.1906554710.1%
 
2.07624268510.1%
 
2.07407617610.1%
 
2.07368731510.1%
 
2.06545138410.1%
 
2.04143524210.1%
 
2.0362327110.1%
 
2.02088165310.1%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7280690371990203e-09
Minimum-1.7743009328842163
Maximum1.7265329360961914
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:42:29.512059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.774300933
5-th percentile-1.57464655
Q1-0.8364807814
median0.03691668622
Q30.8476888686
95-th percentile1.533234942
Maximum1.726532936
Range3.500833869
Interquartile range (IQR)1.68416965

Descriptive statistics

Standard deviation0.9999999988
Coefficient of variation (CV)-578680583.5
Kurtosis-1.167802284
Mean-1.728069037e-09
Median Absolute Deviation (MAD)0.8488046657
Skewness-0.03413738526
Sum-1.728069037e-06
Variance0.9999999977
2020-08-25T00:42:29.615801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.45312225810.1%
 
-1.75908255610.1%
 
0.978455126310.1%
 
0.446620464310.1%
 
1.41538238510.1%
 
-1.4856911910.1%
 
-0.697150230410.1%
 
-1.48177075410.1%
 
-0.465168744310.1%
 
-0.417644351710.1%
 
1.24640381310.1%
 
-1.02082407510.1%
 
-0.839406490310.1%
 
0.199866786610.1%
 
0.639307081710.1%
 
0.42902776610.1%
 
0.842398047410.1%
 
0.43976882110.1%
 
-1.53660678910.1%
 
0.504530727910.1%
 
-0.824838161510.1%
 
-0.186430424510.1%
 
-1.32298517210.1%
 
0.596404254410.1%
 
-0.551390767110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.77430093310.1%
 
-1.7718801510.1%
 
-1.77008044710.1%
 
-1.7664647110.1%
 
-1.76492404910.1%
 
-1.7639590510.1%
 
-1.75908255610.1%
 
-1.75682675810.1%
 
-1.74141454710.1%
 
-1.72819638310.1%
 
ValueCountFrequency (%) 
1.72653293610.1%
 
1.72595238710.1%
 
1.72108185310.1%
 
1.71476364110.1%
 
1.71147036610.1%
 
1.70473635210.1%
 
1.70274794110.1%
 
1.70094668910.1%
 
1.69475901110.1%
 
1.69234085110.1%
 

oz3
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.188630893826485e-10
Minimum-2.0280141830444336
Maximum3.3354096412658687
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:42:29.729748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.028014183
5-th percentile-1.328818214
Q1-0.7578776628
median-0.1966905147
Q30.6001077741
95-th percentile1.93539694
Maximum3.335409641
Range5.363423824
Interquartile range (IQR)1.357985437

Descriptive statistics

Standard deviation0.9999999992
Coefficient of variation (CV)-1927290685
Kurtosis0.02283935967
Mean-5.188630894e-10
Median Absolute Deviation (MAD)0.6493407935
Skewness0.6934067936
Sum-5.188630894e-07
Variance0.9999999983
2020-08-25T00:42:29.828640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.996093273210.1%
 
-0.994767367810.1%
 
-0.633264243610.1%
 
-0.323571145510.1%
 
0.0577801987510.1%
 
-0.106526829310.1%
 
-0.371418327110.1%
 
-0.647131085410.1%
 
2.61976957310.1%
 
-0.459305822810.1%
 
0.732081174910.1%
 
-0.713531017310.1%
 
-0.437819749110.1%
 
-0.940086603210.1%
 
-0.879535913510.1%
 
-1.21609461310.1%
 
2.26476788510.1%
 
-0.979140102910.1%
 
0.136350095310.1%
 
-1.61015367510.1%
 
-0.859993159810.1%
 
0.0201312936810.1%
 
0.179352030210.1%
 
0.139700278610.1%
 
-1.11015701310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.02801418310.1%
 
-2.00786662110.1%
 
-1.97143864610.1%
 
-1.97069156210.1%
 
-1.91131055410.1%
 
-1.88800680610.1%
 
-1.79453444510.1%
 
-1.7763940110.1%
 
-1.74502134310.1%
 
-1.74378800410.1%
 
ValueCountFrequency (%) 
3.33540964110.1%
 
3.30049991610.1%
 
3.10714530910.1%
 
2.99365091310.1%
 
2.9785888210.1%
 
2.90031099310.1%
 
2.88063979110.1%
 
2.75025200810.1%
 
2.69627308810.1%
 
2.61976957310.1%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.90080166951168e-11
Minimum-1.7285093069076538
Maximum1.735319972038269
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:42:29.937638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.728509307
5-th percentile-1.518123949
Q1-0.8708079755
median-0.0001787887522
Q30.8721283227
95-th percentile1.569591773
Maximum1.735319972
Range3.463829279
Interquartile range (IQR)1.742936298

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-1.69468499e+10
Kurtosis-1.19965317
Mean-5.90080167e-11
Median Absolute Deviation (MAD)0.8724511908
Skewness0.04109121716
Sum-5.90080167e-08
Variance1.000000003
2020-08-25T00:42:30.040497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.529295921310.1%
 
-0.068034738310.1%
 
0.678403794810.1%
 
-0.729184031510.1%
 
0.694024980110.1%
 
0.48860660210.1%
 
1.06508016610.1%
 
-0.721362173610.1%
 
1.57944214310.1%
 
0.819014489710.1%
 
0.879560053310.1%
 
-1.51302015810.1%
 
-1.14708042110.1%
 
0.469073444610.1%
 
1.5456643110.1%
 
1.24737620410.1%
 
1.42398917710.1%
 
0.103595629310.1%
 
-0.224768906810.1%
 
-0.695949077610.1%
 
-1.08006894610.1%
 
-0.000188981182910.1%
 
-0.484687209110.1%
 
-0.0850385810.1%
 
-0.567026138310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.72850930710.1%
 
-1.72821915110.1%
 
-1.72572803510.1%
 
-1.72336745310.1%
 
-1.72318422810.1%
 
-1.71635544310.1%
 
-1.71143209910.1%
 
-1.71035695110.1%
 
-1.70588254910.1%
 
-1.70471787510.1%
 
ValueCountFrequency (%) 
1.73531997210.1%
 
1.73503565810.1%
 
1.73266708910.1%
 
1.72911906210.1%
 
1.72680723710.1%
 
1.72121369810.1%
 
1.71658825910.1%
 
1.70735108910.1%
 
1.70521509610.1%
 
1.70018291510.1%
 

oz5
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5227124094963074e-10
Minimum-1.8418147563934328
Maximum4.036691188812257
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:42:30.153697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.841814756
5-th percentile-1.06800766
Q1-0.7349079996
median-0.3202283978
Q30.4882532656
95-th percentile2.130833399
Maximum4.036691189
Range5.878505945
Interquartile range (IQR)1.223161265

Descriptive statistics

Standard deviation0.9999999995
Coefficient of variation (CV)6567228278
Kurtosis1.153622492
Mean1.522712409e-10
Median Absolute Deviation (MAD)0.5098074377
Skewness1.246941809
Sum1.522712409e-07
Variance0.999999999
2020-08-25T00:42:30.265502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0460487902210.1%
 
0.135792985610.1%
 
-0.94816160210.1%
 
2.19744849210.1%
 
-0.834630191310.1%
 
-0.185207292410.1%
 
0.00289405090710.1%
 
1.11456203510.1%
 
-0.818998992410.1%
 
-0.447892308210.1%
 
-1.03438091310.1%
 
-1.15361225610.1%
 
-0.0273024737810.1%
 
1.5051726110.1%
 
-0.803327381610.1%
 
-0.332339435810.1%
 
0.676376640810.1%
 
-0.280202925210.1%
 
-0.486635923410.1%
 
-0.307075798510.1%
 
-0.45831438910.1%
 
-0.283508658410.1%
 
-0.221527174110.1%
 
1.77465534210.1%
 
2.17411398910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.84181475610.1%
 
-1.3886047610.1%
 
-1.38856005710.1%
 
-1.34139251710.1%
 
-1.33835005810.1%
 
-1.3174328810.1%
 
-1.29826617210.1%
 
-1.27938771210.1%
 
-1.27766537710.1%
 
-1.26481568810.1%
 
ValueCountFrequency (%) 
4.03669118910.1%
 
3.70406317710.1%
 
3.53706932110.1%
 
3.47072553610.1%
 
3.42328882210.1%
 
3.32407021510.1%
 
3.24170088810.1%
 
3.20091271410.1%
 
3.13494539310.1%
 
3.13194012610.1%
 

target
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.932808224111796e-10
Minimum-2.685921192169189
Maximum3.2545480728149414
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:42:30.379583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.685921192
5-th percentile-1.841869408
Q1-0.5986173451
median0.1541610584
Q30.7016828656
95-th percentile1.403434706
Maximum3.254548073
Range5.940469265
Interquartile range (IQR)1.300300211

Descriptive statistics

Standard deviation0.9999999999
Coefficient of variation (CV)-2027242809
Kurtosis-0.02723113648
Mean-4.932808224e-10
Median Absolute Deviation (MAD)0.628915742
Skewness-0.2626851478
Sum-4.932808224e-07
Variance0.9999999997
2020-08-25T00:42:30.483900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.224078074110.1%
 
-0.779940068710.1%
 
-0.15299655510.1%
 
0.201984345910.1%
 
1.45833313510.1%
 
1.59251773410.1%
 
-0.157389923910.1%
 
1.17708730710.1%
 
-1.075522910.1%
 
1.08723974210.1%
 
0.492512494310.1%
 
0.157680213510.1%
 
0.288409978210.1%
 
0.723301947110.1%
 
0.973300516610.1%
 
0.774078309510.1%
 
-1.06119513510.1%
 
1.09955930710.1%
 
0.96440547710.1%
 
-0.731104552710.1%
 
-1.34501910210.1%
 
-0.487965315610.1%
 
-0.0855279117810.1%
 
0.19254013910.1%
 
-0.339180797310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.68592119210.1%
 
-2.64436364210.1%
 
-2.50242829310.1%
 
-2.49406099310.1%
 
-2.46361970910.1%
 
-2.41174030310.1%
 
-2.35342383410.1%
 
-2.33232450510.1%
 
-2.32060027110.1%
 
-2.29511499410.1%
 
ValueCountFrequency (%) 
3.25454807310.1%
 
3.14333152810.1%
 
3.1113364710.1%
 
3.08640456210.1%
 
2.84537649210.1%
 
2.78778147710.1%
 
2.68988180210.1%
 
2.56964635810.1%
 
2.43094348910.1%
 
2.3456556810.1%
 

Interactions

2020-08-25T00:42:22.939170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:23.083077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:23.391428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:23.541039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:23.691798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:23.843325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:23.990251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:24.142847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:24.303533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:24.466469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:24.626399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:24.785335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:24.947488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:25.090548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:25.244009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:25.391723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:25.550133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:25.705815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:25.861593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:26.016251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:26.170948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:26.323710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:26.486316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:26.646598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:26.803906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:26.965382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:27.139570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:27.298093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:27.478925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:27.640436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:27.799266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:27.946881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:28.281774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:28.430970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:28.588351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:28.744444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:42:30.595865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:42:30.777411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:42:30.949424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:42:31.128937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:42:28.981447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:29.172263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5target
0-0.7833960.0830350.5506330.060445-0.3900580.729452
10.6065261.4623042.3984821.6967072.1302900.173213
20.9157381.024161-0.317019-0.906453-0.071312-1.738047
30.7893200.6134110.3188790.0332380.341646-1.714836
4-1.369379-1.086501-0.321815-0.203726-0.674851-0.156214
51.4109340.8072020.030256-0.4439590.403562-1.234157
6-0.072172-0.808521-0.6472980.243744-0.4959550.898193
70.8651031.557450-0.711564-1.389076-0.094924-0.803404
80.0494760.6703722.0782251.2557131.0160640.166536
90.1526480.4537270.392260-1.4352310.416229-1.012392

Last rows

oz1oz2oz3oz4oz5target
990-0.979251-1.770080-0.684854-1.080069-0.671299-1.083733
991-0.701897-0.911157-1.070571-1.578649-0.9557840.171807
992-1.173958-1.503535-0.997195-0.472630-1.119810-0.367483
9930.8627840.957748-0.664739-1.253368-0.583192-2.278422
994-1.767933-1.0394080.5468110.816105-0.492845-0.152997
995-1.193054-0.7366000.8108071.181978-0.3225180.679018
9960.213934-0.5575550.0172680.4709360.0951791.255707
9970.0637690.547012-0.437820-0.640083-0.332339-0.875029
9980.8660550.1271641.0162941.0959371.113118-0.382205
9990.7527441.1010620.9409430.1874230.629634-1.472828